## Nombre de participants se déclarant comme joueurs :  29
## Nombre de femmes se déclarant comme joueuses :  3
## Age médian des joueurs :  15

Removing Outliers based on BET

(pas nécessaire pour la mesure basée sur l’échelle de confiance)

{r removing.outliers.setup.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SETUP # #------------------------------------------------------ # # DTM <- DTAll[which(DTAll$nom_du_jeu=="Motrice"),] # DTL <- DTAll[which(DTAll$nom_du_jeu=="Logique2"),] # DTS <- DTAll[which(DTAll$nom_du_jeu=="Sensoriel"),] # # # get.outliers <- function(DTDescMLoc,DTDescSLoc,DTDescLLoc){ # outliersM <- boxplot.stats(DTDescMLoc$var)$out # outliersS <- boxplot.stats(DTDescSLoc$var)$out # outliersL <- boxplot.stats(DTDescLLoc$var)$out # # outliers = data.table(type=character(0),id=character(0)) # setkey(outliers,id) # if(length(outliersM) > 0) # outliers = merge(outliers,data.table(id=DTDescMLoc[var %in% outliersM]$IDjoueur,type="Moteur"),by=c("id","type"),all=TRUE) # if(length(outliersS) > 0) # outliers = merge(outliers,data.table(id=DTDescSLoc[var %in% outliersS]$IDjoueur,type="Sensoriel"),by=c("id","type"),all=TRUE) # if(length(outliersL) > 0) # outliers = merge(outliers,data.table(id=DTDescLLoc[var %in% outliersL]$IDjoueur,type="Logique"),by=c("id","type"),all=TRUE) # # return(outliers) # } # # plot.outliers <- function(DT,title){ # p <- ggplot(DT, # aes(type,var)) + # xlab("Difficulty Type") + # ylab(title) # p <- p + geom_boxplot() + geom_point(shape=1) # print(p) # } #

{r detect.outliers.bet.sd, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS BET STD DEV # #------------------------------------------------------ # DTDescM = DTM[,.(type="Moteur",var=sd(miseNorm)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=sd(miseNorm)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=sd(miseNorm)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Bet Standard Dev"); # # outliers = get.outliers(DTDescM,DTDescS,DTDescL) # print(paste("Outliers BET STANDARD DEVIATION:",toString(outliers$id))) # # DTM[IDjoueur %in% unlist(outliers[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliers[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Logical Task");NULL},by=.(IDjoueur)] #

{r detect.outliers.win.sum.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUM OF WINS # #------------------------------------------------------ # # Difficulty : win sum # # # DTDescM = DTM[,.(type="Moteur",var=sum(gagnant)),by=IDjoueur] # # DTDescS = DTS[,.(type="Sensoriel",var=sum(gagnant)),by=IDjoueur] # # DTDescL = DTL[,.(type="Logique",var=sum(gagnant)),by=IDjoueur] # # # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win Sum"); # # # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # # print(paste("Outliers :",toString(outliersLoc$id))) # # # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Motor Task");NULL},by=.(IDjoueur)] # # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Sensory Task");NULL},by=.(IDjoueur)] # # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Logical Task");NULL},by=.(IDjoueur)] # #

{r detect.outliers.sheeps.saved.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SAVED SHEEPS # #------------------------------------------------------ # # Difficulty and strategy = saved sheeps # DTDescM = DTM[,.(type="Moteur",var=max(moutons_sauves)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=max(moutons_sauves)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=max(moutons_sauves)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Saved sheeps"); # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # print(paste("Outliers BET SAVED SHEEPS:",toString(outliersLoc$id))) # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Score Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Score Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Score Logical Task");NULL},by=.(IDjoueur)] # #

{r detect.outliers.dda.exploit.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS EXPLOIT DDA # #------------------------------------------------------ # # DDA Exploit : Win/Fail delta sum max # DTDescM = DTM[,.(type="Moteur",var=max(cumulDeltaMise)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=max(cumulDeltaMise)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=max(cumulDeltaMise)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win/Fail delta sum max"); # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # print(paste("Outliers BET EXPLOIT DDA:",toString(outliersLoc$id))) # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Logical Task");NULL},by=.(IDjoueur)] #

{r detect.outliers.summary.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUMMARY # #------------------------------------------------------ # print(paste("Total number of outliers: ",toString(nrow(unique(outliers,by="id"))))) # print(paste("Total number of outliers motor task: ",toString(nrow(unique(outliers[type=="Moteur"],by="id"))))) # print(paste("Total number of outliers perceptive task: ",toString(nrow(unique(outliers[type=="Logique"],by="id"))))) # print(paste("Total number of outliers logical task: ",toString(nrow(unique(outliers[type=="Sensoriel"],by="id"))))) #

{r remove.outliers.bet, echo=FALSE} # #------------------------------------------------------ # # REMOVING OUTLIERS FROM TABLES # #------------------------------------------------------ # # removing all outliers # DTM <- DTM[!IDjoueur %in% unlist(outliers[type=="Moteur"]$id)] # DTS <- DTS[!IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id)] # DTL <- DTL[!IDjoueur %in% unlist(outliers[type=="Logique"]$id)] # DTAll <- data.table() # DTAll <- rbind(DTAll,DTL) # DTAll <- rbind(DTAll,DTM) # DTAll <- rbind(DTAll,DTS) #

Removing Outliers based on CONFIDENCE SCALE

## [1] "Outliers CS STANDARD DEVIATION: 9b3ph38yc, 9b3ph38yc, a6dfu5ljd, a6dfu5ljd, bzrji9dqz, dyg7cga2o, dyg7cga2o, ejodnl05c, kctu3te1y, tmxmxmwhi, zp9bc59o5, zv35u39vc"
## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of outliers:  9"
## [1] "Total number of outliers motor task:  0"
## [1] "Total number of outliers perceptive task:  5"
## [1] "Total number of outliers logical task:  7"

Modeling difficulties

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   2016.5   2038.2  -1004.3   2008.5     1678 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1935 -0.7469  0.2908  0.7381  2.8784 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.559    0.7476  
## Number of obs: 1682, groups:  IDjoueur, 58
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.0580     0.1843   -5.74 9.48e-09 ***
## difficulty    3.0160     0.2115   14.26  < 2e-16 ***
## timeNorm     -0.5213     0.1990   -2.62  0.00879 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.540       
## timeNorm   -0.572 -0.008
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0      1682         0 
## [1] "Player levels from ranef:"
##   (Intercept)      
##  Min.   :-1.05422  
##  1st Qu.:-0.44100  
##  Median :-0.11748  
##  Mean   :-0.00241  
##  3rd Qu.: 0.33077  
##  Max.   : 1.65790  
## [1] "Intercept: -1.06 9.5e-09 ***"
## [1] "Difficulty: 3.02 3.8e-46 ***"
## [1] "Time: -0.521 0.0088 **"
## [1] "R2 fixed: 0.17"
## [1] "R2 mixed: 0.29"
## [1] "Cross Val: 0.68"
## [1] "AIC: 2000"
##         0%        25%        50%        75%       100% 
## -1.6579021 -0.3307656  0.1174780  0.4410031  1.0542161

##         0%        25%        50%        75%       100% 
## -1.6579021 -0.3307656  0.1174780  0.4410031  1.0542161

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1157.7   1178.9   -574.8   1149.7     1475 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.1882 -0.3711  0.1173  0.3487  6.1614 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.7454   0.8634  
## Number of obs: 1479, groups:  IDjoueur, 51
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -3.1584     0.2662 -11.864   <2e-16 ***
## difficulty    8.0854     0.4166  19.407   <2e-16 ***
## timeNorm     -0.4665     0.2800  -1.666   0.0957 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.633       
## timeNorm   -0.507 -0.075
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge: degenerate Hessian with 1 negative
## eigenvalues
## The result is correct only if all data used by the model has not changed since model was fitted.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : unable to evaluate scaled gradient

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge: degenerate Hessian with 1 negative
## eigenvalues
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0         0      1479 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.664795  
##  1st Qu.:-0.448260  
##  Median : 0.051307  
##  Mean   :-0.001189  
##  3rd Qu.: 0.429804  
##  Max.   : 1.509537  
## [1] "Intercept: -3.16 1.8e-32 ***"
## [1] "Difficulty: 8.09 6.7e-84 ***"
## [1] "Time: -0.467 0.096 ."
## [1] "R2 fixed: 0.29"
## [1] "R2 mixed: 0.45"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1200"
##          0%         25%         50%         75%        100% 
## -1.50953716 -0.42980436 -0.05130702  0.44825963  1.66479506

##          0%         25%         50%         75%        100% 
## -1.50953716 -0.42980436 -0.05130702  0.44825963  1.66479506

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1444.5   1465.8   -718.2   1436.5     1533 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.0357 -0.4980 -0.1017  0.5004  5.0622 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.57     1.253   
## Number of obs: 1537, groups:  IDjoueur, 53
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.9054     0.2628  -7.251 4.14e-13 ***
## difficulty    5.7562     0.3198  18.001  < 2e-16 ***
## timeNorm     -1.9355     0.2564  -7.550 4.35e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.497       
## timeNorm   -0.376 -0.233
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##      1537         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.8051717  
##  1st Qu.:-0.7513212  
##  Median :-0.2064150  
##  Mean   :-0.0003176  
##  3rd Qu.: 0.7228639  
##  Max.   : 3.1492300  
## [1] "Intercept: -1.91 4.1e-13 ***"
## [1] "Difficulty: 5.76 1.9e-72 ***"
## [1] "Time: -1.94 4.4e-14 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.8"
## [1] "AIC: 1400"
##         0%        25%        50%        75%       100% 
## -3.1492300 -0.7228639  0.2064150  0.7513212  1.8051717

##         0%        25%        50%        75%       100% 
## -3.1492300 -0.7228639  0.2064150  0.7513212  1.8051717

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3393, p-value = 0.1805
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1375478

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.86499, p-value = 0.387
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.09516712

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.12965, p-value = 0.8968
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.01388433

Playing board games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.86388, p-value = 0.3877
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.08757052

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.29511, p-value = 0.7679
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03198946

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.6523, p-value = 0.5142
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.06919576

Self efficacy and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 29 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.16967, p-value = 0.8653
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02270513
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 24 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.4333, p-value = 0.01496
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.3398094 
## 
## [1] "self.eff.on.level.s 0.34 0.015 *"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 27 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.46598, p-value = 0.6412
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.06648267

Risk aversion and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3157, p-value = 0.1883
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##      tau 
## 0.127906

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.0165, p-value = 0.04374
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2093532 
## 
## [1] "risk.av.on.level.s 0.21 0.044 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3781, p-value = 0.1682
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1404273

Age and level for each task

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.1261, p-value = 0.2601
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1063448
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.8814, p-value = 0.05991
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1899593 
## 
## [1] "age.on.level.s 0.19 0.06 ."
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.1451, p-value = 0.2522
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1130316

Sex and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.3774, p-value = 0.01743
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2593202 
## 
## [1] "sexe.on.level.m -0.26 0.017 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.0609, p-value = 0.9514
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## 0.007100716

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.38949, p-value = 0.6969
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.04451521

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 227, p-value = 0.01687
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.8465888 -0.1080105
## sample estimates:
## difference in location 
##             -0.4966452 
## 
## [1] "sexe.on.level.m.2 -0.5 0.017 * mean(A): 0.16 mean(B): -0.32"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 283, p-value = 0.96
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.4684763  0.5456280
## sample estimates:
## difference in location 
##             0.01412646

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 302, p-value = 0.7064
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.7753238  0.5708569
## sample estimates:
## difference in location 
##            -0.06017729

CONFIDENCE SCALE APPROACH

For Bet approach, see the other file.

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.079 44   0.0014 **
##  2:      0.09375          0.120 55 3.5e-05 ***
##  3:      0.15625          0.110 57 0.00021 ***
##  4:      0.21875          0.150 58   1e-06 ***
##  5:      0.28125          0.120 56 1.5e-05 ***
##  6:      0.34375          0.100 57 3.5e-05 ***
##  7:      0.40625          0.086 56   0.0098 **
##  8:      0.46875          0.015 57     0.41 :(
##  9:      0.53125         -0.010 56     0.39 :(
## 10:      0.59375         -0.062 58    0.003 **
## 11:      0.65625         -0.098 58 7.7e-05 ***
## 12:      0.71875         -0.120 57 5.3e-06 ***
## 13:      0.78125         -0.170 55   9e-08 ***
## 14:      0.84375         -0.210 52 3.2e-08 ***
## 15:      0.90625         -0.230 55 4.5e-10 ***
## 16:      0.96875         -0.170 55 1.7e-09 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 44   0.0014 **
##  2: 55 3.5e-05 ***
##  3: 57 0.00021 ***
##  4: 58   1e-06 ***
##  5: 56 1.5e-05 ***
##  6: 57 3.5e-05 ***
##  7: 56   0.0098 **
##  8: 57     0.41 :(
##  9: 56     0.39 :(
## 10: 58    0.003 **
## 11: 58 7.7e-05 ***
## 12: 57 5.3e-06 ***
## 13: 55   9e-08 ***
## 14: 52 3.2e-08 ***
## 15: 55 4.5e-10 ***
## 16: 55 1.7e-09 ***
## [1] 55.4
## [1] 3.4

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0690 23     0.13 :(
##  2:      0.09375         0.0560 28      0.3 :(
##  3:      0.15625         0.0940 38     0.12 :(
##  4:      0.21875         0.1100 37   0.0034 **
##  5:      0.28125         0.0940 36     0.019 *
##  6:      0.34375         0.0780 37     0.016 *
##  7:      0.40625         0.0640 36     0.033 *
##  8:      0.46875         0.0310 35     0.25 :(
##  9:      0.53125        -0.0062 34     0.66 :(
## 10:      0.59375        -0.0940 36     0.028 *
## 11:      0.65625        -0.1600 35   0.0015 **
## 12:      0.71875        -0.1900 34 6.8e-06 ***
## 13:      0.78125        -0.1900 31 0.00014 ***
## 14:      0.84375        -0.3100 16   0.0039 **
## 15:      0.90625        -0.2700 20 0.00017 ***
## 16:      0.96875        -0.1100 17     0.056 .
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 23     0.13 :(
##  2: 28      0.3 :(
##  3: 38     0.12 :(
##  4: 37   0.0034 **
##  5: 36     0.019 *
##  6: 37     0.016 *
##  7: 36     0.033 *
##  8: 35     0.25 :(
##  9: 34     0.66 :(
## 10: 36     0.028 *
## 11: 35   0.0015 **
## 12: 34 6.8e-06 ***
## 13: 31 0.00014 ***
## 14: 16   0.0039 **
## 15: 20 0.00017 ***
## 16: 17     0.056 .
## [1] 30.8
## [1] 7.57

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0540 31      0.01 *
##  2:      0.09375         0.1400 35   3e-04 ***
##  3:      0.15625         0.1100 36   0.0053 **
##  4:      0.21875         0.1500 37 0.00064 ***
##  5:      0.28125         0.1600 39 0.00052 ***
##  6:      0.34375         0.1200 37     0.011 *
##  7:      0.40625         0.0590 38      0.2 :(
##  8:      0.46875        -0.0170 36     0.72 :(
##  9:      0.53125        -0.0013 37     0.93 :(
## 10:      0.59375        -0.0600 34     0.11 :(
## 11:      0.65625        -0.1300 41   0.0014 **
## 12:      0.71875        -0.0690 38     0.024 *
## 13:      0.78125        -0.1400 39   2e-04 ***
## 14:      0.84375        -0.1800 37 1.3e-05 ***
## 15:      0.90625        -0.2100 36 1.1e-06 ***
## 16:      0.96875        -0.1200 30 4.2e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 31      0.01 *
##  2: 35   3e-04 ***
##  3: 36   0.0053 **
##  4: 37 0.00064 ***
##  5: 39 0.00052 ***
##  6: 37     0.011 *
##  7: 38      0.2 :(
##  8: 36     0.72 :(
##  9: 37     0.93 :(
## 10: 34     0.11 :(
## 11: 41   0.0014 **
## 12: 38     0.024 *
## 13: 39   2e-04 ***
## 14: 37 1.3e-05 ***
## 15: 36 1.1e-06 ***
## 16: 30 4.2e-05 ***
## [1] 36.3
## [1] 2.82

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  1          NA
##  2:      0.09375         0.1600 11     0.12 :(
##  3:      0.15625         0.1300 14     0.016 *
##  4:      0.21875         0.0730 15     0.093 .
##  5:      0.28125         0.2200 13     0.011 *
##  6:      0.34375         0.1600 14   0.0021 **
##  7:      0.40625         0.1600 15     0.093 .
##  8:      0.46875         0.0310 18     0.085 .
##  9:      0.53125        -0.0560 17      0.07 .
## 10:      0.59375        -0.0940 19     0.45 :(
## 11:      0.65625        -0.0062 17     0.92 :(
## 12:      0.71875        -0.0690 19      0.06 .
## 13:      0.78125        -0.1600 19   0.0073 **
## 14:      0.84375        -0.2300 21 0.00032 ***
## 15:      0.90625        -0.2100 22 0.00018 ***
## 16:      0.96875        -0.3100 21 6.3e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 11     0.12 :(
##  2: 14     0.016 *
##  3: 15     0.093 .
##  4: 13     0.011 *
##  5: 14   0.0021 **
##  6: 15     0.093 .
##  7: 18     0.085 .
##  8: 17      0.07 .
##  9: 19     0.45 :(
## 10: 17     0.92 :(
## 11: 19      0.06 .
## 12: 19   0.0073 **
## 13: 21 0.00032 ***
## 14: 22 0.00018 ***
## 15: 21 6.3e-05 ***
## [1] 17
## [1] 3.25
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Motor task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375          0.094  9     0.63 :(
##  3:      0.15625          0.094 29     0.43 :(
##  4:      0.21875          0.069 41     0.037 *
##  5:      0.28125          0.094 47     0.018 *
##  6:      0.34375          0.110 50     0.013 *
##  7:      0.40625          0.069 50     0.074 .
##  8:      0.46875          0.040 51     0.036 *
##  9:      0.53125          0.035 54     0.15 :(
## 10:      0.59375         -0.029 53     0.41 :(
## 11:      0.65625         -0.081 54   0.0085 **
## 12:      0.71875         -0.069 54   0.0029 **
## 13:      0.78125         -0.110 45 0.00073 ***
## 14:      0.84375         -0.170 29   0.0045 **
## 15:      0.90625         -0.210 15     0.018 *
## 16:      0.96875         -0.270  6     0.056 .
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  9     0.63 :(
##  2: 29     0.43 :(
##  3: 41     0.037 *
##  4: 47     0.018 *
##  5: 50     0.013 *
##  6: 50     0.074 .
##  7: 51     0.036 *
##  8: 54     0.15 :(
##  9: 53     0.41 :(
## 10: 54   0.0085 **
## 11: 54   0.0029 **
## 12: 45 0.00073 ***
## 13: 29   0.0045 **
## 14: 15     0.018 *
## 15:  6     0.056 .
## [1] 39.1
## [1] 17.2
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375         0.0940  9   0.63 :(
##  3:      0.15625         0.0940 26    0.4 :(
##  4:      0.21875         0.0790 27   0.073 .
##  5:      0.28125         0.1200 25   0.017 *
##  6:      0.34375         0.1100 27 0.0014 **
##  7:      0.40625         0.0690 26   0.032 *
##  8:      0.46875         0.0810 25 0.0095 **
##  9:      0.53125         0.0690 25   0.14 :(
## 10:      0.59375         0.0062 24   0.92 :(
## 11:      0.65625        -0.0400 25   0.33 :(
## 12:      0.71875        -0.0880 24   0.023 *
## 13:      0.78125        -0.0810 15   0.037 *
## 14:      0.84375             NA  0        NA
## 15:      0.90625             NA  0        NA
## 16:      0.96875             NA  0        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  9   0.63 :(
##  2: 26    0.4 :(
##  3: 27   0.073 .
##  4: 25   0.017 *
##  5: 27 0.0014 **
##  6: 26   0.032 *
##  7: 25 0.0095 **
##  8: 25   0.14 :(
##  9: 24   0.92 :(
## 10: 25   0.33 :(
## 11: 24   0.023 *
## 12: 15   0.037 *
## [1] 23.2
## [1] 5.46
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375             NA  0      NA
##  3:      0.15625             NA  3      NA
##  4:      0.21875         0.0670 14 0.29 :(
##  5:      0.28125         0.0690 21 0.31 :(
##  6:      0.34375         0.0460 22 0.67 :(
##  7:      0.40625         0.0190 22 0.92 :(
##  8:      0.46875        -0.0021 22 0.97 :(
##  9:      0.53125         0.0350 22 0.24 :(
## 10:      0.59375        -0.0770 22  0.2 :(
## 11:      0.65625        -0.1200 22 0.019 *
## 12:      0.71875        -0.0440 23 0.17 :(
## 13:      0.78125        -0.0810 22 0.079 .
## 14:      0.84375        -0.1800 21 0.024 *
## 15:      0.90625        -0.1900  7 0.15 :(
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1: 14 0.29 :(
##  2: 21 0.31 :(
##  3: 22 0.67 :(
##  4: 22 0.92 :(
##  5: 22 0.97 :(
##  6: 22 0.24 :(
##  7: 22  0.2 :(
##  8: 22 0.019 *
##  9: 23 0.17 :(
## 10: 22 0.079 .
## 11: 21 0.024 *
## 12:  7 0.15 :(
## [1] 20
## [1] 4.71
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 1      NA
##  6:      0.34375             NA 1      NA
##  7:      0.40625          0.190 2  0.5 :(
##  8:      0.46875             NA 4      NA
##  9:      0.53125         -0.031 7 0.19 :(
## 10:      0.59375         -0.094 7 0.33 :(
## 11:      0.65625         -0.160 7 0.33 :(
## 12:      0.71875         -0.085 7 0.15 :(
## 13:      0.78125         -0.180 8 0.028 *
## 14:      0.84375         -0.160 8  0.1 :(
## 15:      0.90625         -0.210 8 0.055 .
## 16:      0.96875         -0.270 6 0.056 .
## [1] "mean and sd of nb players per bin"
##    nb    pval
## 1:  2  0.5 :(
## 2:  7 0.19 :(
## 3:  7 0.33 :(
## 4:  7 0.33 :(
## 5:  7 0.15 :(
## 6:  8 0.028 *
## 7:  8  0.1 :(
## 8:  8 0.055 .
## 9:  6 0.056 .
## [1] 6.67
## [1] 1.87
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0250 39     0.19 :(
##  2:      0.09375         0.0310 48     0.24 :(
##  3:      0.15625         0.0940 47     0.27 :(
##  4:      0.21875         0.0310 36     0.41 :(
##  5:      0.28125         0.0190 35     0.97 :(
##  6:      0.34375        -0.0190 29     0.74 :(
##  7:      0.40625        -0.0062 31     0.85 :(
##  8:      0.46875        -0.1200 31     0.031 *
##  9:      0.53125        -0.1800 28    0.004 **
## 10:      0.59375        -0.1900 34   0.0012 **
## 11:      0.65625        -0.1600 36 0.00091 ***
## 12:      0.71875        -0.2200 35 0.00034 ***
## 13:      0.78125        -0.2300 34 1.3e-05 ***
## 14:      0.84375        -0.2400 39 2.9e-05 ***
## 15:      0.90625        -0.2100 49 4.6e-08 ***
## 16:      0.96875        -0.0940 51 1.2e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 39     0.19 :(
##  2: 48     0.24 :(
##  3: 47     0.27 :(
##  4: 36     0.41 :(
##  5: 35     0.97 :(
##  6: 29     0.74 :(
##  7: 31     0.85 :(
##  8: 31     0.031 *
##  9: 28    0.004 **
## 10: 34   0.0012 **
## 11: 36 0.00091 ***
## 12: 35 0.00034 ***
## 13: 34 1.3e-05 ***
## 14: 39 2.9e-05 ***
## 15: 49 4.6e-08 ***
## 16: 51 1.2e-06 ***
## [1] 37.6
## [1] 7.34

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125        8.4e-05 17      1 :(
##  2:      0.09375       -4.4e-02 16   0.36 :(
##  3:      0.15625        9.4e-02 15   0.79 :(
##  4:      0.21875        3.1e-02  9   0.63 :(
##  5:      0.28125        1.9e-02 12   0.91 :(
##  6:      0.34375       -1.7e-01 10   0.066 .
##  7:      0.40625       -1.6e-01  9   0.12 :(
##  8:      0.46875       -2.2e-01 13   0.017 *
##  9:      0.53125       -2.8e-01  9   0.057 .
## 10:      0.59375       -3.4e-01 12 0.0082 **
## 11:      0.65625       -2.8e-01 12 0.0024 **
## 12:      0.71875       -4.5e-01 11 0.0036 **
## 13:      0.78125       -2.8e-01 11 0.0086 **
## 14:      0.84375       -3.2e-01 13 0.0095 **
## 15:      0.90625       -2.0e-01 16 0.0017 **
## 16:      0.96875       -1.1e-01 17   0.056 .
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1: 17      1 :(
##  2: 16   0.36 :(
##  3: 15   0.79 :(
##  4:  9   0.63 :(
##  5: 12   0.91 :(
##  6: 10   0.066 .
##  7:  9   0.12 :(
##  8: 13   0.017 *
##  9:  9   0.057 .
## 10: 12 0.0082 **
## 11: 12 0.0024 **
## 12: 11 0.0036 **
## 13: 11 0.0086 **
## 14: 13 0.0095 **
## 15: 16 0.0017 **
## 16: 17   0.056 .
## [1] 12.6
## [1] 2.83

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.052 22     0.11 :(
##  2:      0.09375          0.031 24     0.29 :(
##  3:      0.15625         -0.023 22     0.51 :(
##  4:      0.21875         -0.019 19     0.98 :(
##  5:      0.28125         -0.012 16      0.9 :(
##  6:      0.34375          0.056 14     0.49 :(
##  7:      0.40625          0.019 17      0.7 :(
##  8:      0.46875         -0.069 14     0.61 :(
##  9:      0.53125         -0.110 14     0.16 :(
## 10:      0.59375         -0.069 15     0.38 :(
## 11:      0.65625         -0.160 18     0.059 .
## 12:      0.71875         -0.120 16     0.14 :(
## 13:      0.78125         -0.160 18   0.0067 **
## 14:      0.84375         -0.190 18     0.011 *
## 15:      0.90625         -0.180 23 0.00035 ***
## 16:      0.96875         -0.056 24 0.00086 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 22     0.11 :(
##  2: 24     0.29 :(
##  3: 22     0.51 :(
##  4: 19     0.98 :(
##  5: 16      0.9 :(
##  6: 14     0.49 :(
##  7: 17      0.7 :(
##  8: 14     0.61 :(
##  9: 14     0.16 :(
## 10: 15     0.38 :(
## 11: 18     0.059 .
## 12: 16     0.14 :(
## 13: 18   0.0067 **
## 14: 18     0.011 *
## 15: 23 0.00035 ***
## 16: 24 0.00086 ***
## [1] 18.4
## [1] 3.59

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375          0.160  8    0.1 :(
##  3:      0.15625          0.220 10   0.024 *
##  4:      0.21875          0.089  8   0.29 :(
##  5:      0.28125          0.069  7    0.8 :(
##  6:      0.34375          0.090  5   0.18 :(
##  7:      0.40625          0.160  5   0.28 :(
##  8:      0.46875             NA  4        NA
##  9:      0.53125         -0.180  5   0.058 .
## 10:      0.59375         -0.140  7    0.02 *
## 11:      0.65625         -0.110  6   0.83 :(
## 12:      0.71875         -0.094  8   0.29 :(
## 13:      0.78125         -0.280  5   0.054 .
## 14:      0.84375         -0.190  8   0.057 .
## 15:      0.90625         -0.290 10   0.011 *
## 16:      0.96875         -0.240 10 0.0059 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  8    0.1 :(
##  2: 10   0.024 *
##  3:  8   0.29 :(
##  4:  7    0.8 :(
##  5:  5   0.18 :(
##  6:  5   0.28 :(
##  7:  5   0.058 .
##  8:  7    0.02 *
##  9:  6   0.83 :(
## 10:  8   0.29 :(
## 11:  5   0.054 .
## 12:  8   0.057 .
## 13: 10   0.011 *
## 14: 10 0.0059 **
## [1] 7.29
## [1] 1.9
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

Logical task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.094 36   0.0044 **
##  2:      0.09375          0.160 41 3.1e-05 ***
##  3:      0.15625          0.170 42 8.4e-05 ***
##  4:      0.21875          0.260 44 3.2e-06 ***
##  5:      0.28125          0.220 36 0.00012 ***
##  6:      0.34375          0.160 40 5.4e-05 ***
##  7:      0.40625          0.094 44   0.0061 **
##  8:      0.46875          0.031 41     0.038 *
##  9:      0.53125         -0.031 38      0.5 :(
## 10:      0.59375         -0.044 42     0.41 :(
## 11:      0.65625         -0.056 40     0.46 :(
## 12:      0.71875         -0.069 39   0.0097 **
## 13:      0.78125         -0.150 44 0.00022 ***
## 14:      0.84375         -0.230 43 2.1e-07 ***
## 15:      0.90625         -0.260 42 4.7e-07 ***
## 16:      0.96875         -0.350 27 6.1e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 36   0.0044 **
##  2: 41 3.1e-05 ***
##  3: 42 8.4e-05 ***
##  4: 44 3.2e-06 ***
##  5: 36 0.00012 ***
##  6: 40 5.4e-05 ***
##  7: 44   0.0061 **
##  8: 41     0.038 *
##  9: 38      0.5 :(
## 10: 42     0.41 :(
## 11: 40     0.46 :(
## 12: 39   0.0097 **
## 13: 44 0.00022 ***
## 14: 43 2.1e-07 ***
## 15: 42 4.7e-07 ***
## 16: 27 6.1e-06 ***
## [1] 39.9
## [1] 4.3

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125          0.150 13 0.035 *
##  2:      0.09375          0.160 13 0.025 *
##  3:      0.15625          0.130 12 0.077 .
##  4:      0.21875          0.230 12 0.016 *
##  5:      0.28125         -0.031  8 0.84 :(
##  6:      0.34375          0.160 13 0.15 :(
##  7:      0.40625          0.094 12 0.19 :(
##  8:      0.46875          0.031 11 0.16 :(
##  9:      0.53125         -0.031 11 0.22 :(
## 10:      0.59375         -0.094 11 0.16 :(
## 11:      0.65625         -0.160  7  0.2 :(
## 12:      0.71875         -0.220  9 0.011 *
## 13:      0.78125         -0.180 10 0.024 *
## 14:      0.84375         -0.340  7 0.031 *
## 15:      0.90625         -0.480  6 0.036 *
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1: 13 0.035 *
##  2: 13 0.025 *
##  3: 12 0.077 .
##  4: 12 0.016 *
##  5:  8 0.84 :(
##  6: 13 0.15 :(
##  7: 12 0.19 :(
##  8: 11 0.16 :(
##  9: 11 0.22 :(
## 10: 11 0.16 :(
## 11:  7  0.2 :(
## 12:  9 0.011 *
## 13: 10 0.024 *
## 14:  7 0.031 *
## 15:  6 0.036 *
## [1] 10.3
## [1] 2.38
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0520 22     0.11 :(
##  2:      0.09375         0.1800 25 0.00046 ***
##  3:      0.15625         0.2300 25   4e-04 ***
##  4:      0.21875         0.2800 24 0.00021 ***
##  5:      0.28125         0.2400 21 0.00036 ***
##  6:      0.34375         0.1800 17   0.0051 **
##  7:      0.40625         0.1200 21     0.039 *
##  8:      0.46875         0.0310 20     0.25 :(
##  9:      0.53125         0.0021 18     0.96 :(
## 10:      0.59375        -0.0260 20     0.75 :(
## 11:      0.65625        -0.1100 23     0.26 :(
## 12:      0.71875        -0.0190 20     0.42 :(
## 13:      0.78125        -0.1300 23    0.007 **
## 14:      0.84375        -0.2200 24 0.00015 ***
## 15:      0.90625        -0.2100 22 0.00081 ***
## 16:      0.96875        -0.3700 13   0.0019 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 22     0.11 :(
##  2: 25 0.00046 ***
##  3: 25   4e-04 ***
##  4: 24 0.00021 ***
##  5: 21 0.00036 ***
##  6: 17   0.0051 **
##  7: 21     0.039 *
##  8: 20     0.25 :(
##  9: 18     0.96 :(
## 10: 20     0.75 :(
## 11: 23     0.26 :(
## 12: 20     0.42 :(
## 13: 23    0.007 **
## 14: 24 0.00015 ***
## 15: 22 0.00081 ***
## 16: 13   0.0019 **
## [1] 21.1
## [1] 3.18

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  1        NA
##  2:      0.09375             NA  3        NA
##  3:      0.15625         0.0940  5   0.78 :(
##  4:      0.21875         0.0810  8   0.23 :(
##  5:      0.28125         0.3300  7   0.051 .
##  6:      0.34375         0.1600 10 0.0098 **
##  7:      0.40625         0.1800 11   0.17 :(
##  8:      0.46875         0.0810 10   0.31 :(
##  9:      0.53125        -0.0310  9   0.91 :(
## 10:      0.59375         0.0063 11   0.82 :(
## 11:      0.65625         0.0690 10   0.22 :(
## 12:      0.71875        -0.0690 10   0.36 :(
## 13:      0.78125        -0.0810 11   0.27 :(
## 14:      0.84375        -0.2300 12 0.0066 **
## 15:      0.90625        -0.2300 14 0.0038 **
## 16:      0.96875        -0.3400 14 0.0011 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  5   0.78 :(
##  2:  8   0.23 :(
##  3:  7   0.051 .
##  4: 10 0.0098 **
##  5: 11   0.17 :(
##  6: 10   0.31 :(
##  7:  9   0.91 :(
##  8: 11   0.82 :(
##  9: 10   0.22 :(
## 10: 10   0.36 :(
## 11: 11   0.27 :(
## 12: 12 0.0066 **
## 13: 14 0.0038 **
## 14: 14 0.0011 **
## [1] 10.1
## [1] 2.44
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.71195  -0.16836   0.00376   0.17619   0.63833  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.182297   0.019774   9.219   <2e-16 ***
## timeNorm     0.005893   0.020913   0.282    0.778    
## obj.diff    -0.375586   0.025858 -14.525   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05568596)
## 
##     Null deviance: 105.649  on 1681  degrees of freedom
## Residual deviance:  93.497  on 1679  degrees of freedom
## AIC: -79.355
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.81749  -0.18021  -0.03534   0.21272   0.81986  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.04407    0.01847   2.386   0.0172 *  
## timeNorm     0.05227    0.02452   2.132   0.0332 *  
## obj.diff    -0.27424    0.01908 -14.376   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06916715)
## 
##     Null deviance: 116.76  on 1478  degrees of freedom
## Residual deviance: 102.09  on 1476  degrees of freedom
## AIC: 251.47
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.73430  -0.20594  -0.01949   0.19850   0.71398  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.21759    0.02001   10.88   <2e-16 ***
## timeNorm     0.05914    0.02495    2.37   0.0179 *  
## obj.diff    -0.53045    0.02119  -25.04   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06995631)
## 
##     Null deviance: 156.54  on 1536  degrees of freedom
## Residual deviance: 107.31  on 1534  degrees of freedom
## AIC: 278.57
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean    error.diff   n    pval
##  1:      1.5      0.5422414     0.6014885 -0.0544661582 116 0.038 *
##  2:      4.5      0.5367816     0.5712048 -0.0274664169 174 0.17 :(
##  3:      7.5      0.5155172     0.5413406 -0.0217582878 174 0.28 :(
##  4:     10.5      0.5413793     0.5404361  0.0102982443 174 0.62 :(
##  5:     13.5      0.5155172     0.5181081 -0.0005152110 174 0.97 :(
##  6:     16.5      0.5310345     0.5333167 -0.0007660154 174 0.97 :(
##  7:     19.5      0.5063218     0.5344527 -0.0290711237 174 0.12 :(
##  8:     22.5      0.4873563     0.4934513 -0.0053069701 174  0.8 :(
##  9:     25.5      0.4890805     0.4822968  0.0047959969 174  0.8 :(
## 10:     28.5      0.4741379     0.4548030  0.0173421720 174  0.4 :(
##     time    error.diff shapes
##  1:  1.5 -0.0544661582     24
##  2:  4.5 -0.0274664169     16
##  3:  7.5 -0.0217582878     16
##  4: 10.5  0.0102982443     16
##  5: 13.5 -0.0005152110     16
##  6: 16.5 -0.0007660154     16
##  7: 19.5 -0.0290711237     16
##  8: 22.5 -0.0053069701     16
##  9: 25.5  0.0047959969     16
## 10: 28.5  0.0173421720     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4676471     0.5966972 -0.13741579 102 2.6e-05 ***
##  2:      4.5      0.5071895     0.6251604 -0.10090751 153 1.4e-07 ***
##  3:      7.5      0.4653595     0.5391838 -0.07478623 153 0.00061 ***
##  4:     10.5      0.5163399     0.5905380 -0.07040539 153   3e-04 ***
##  5:     13.5      0.4673203     0.5737449 -0.09361413 153 4.6e-07 ***
##  6:     16.5      0.4196078     0.5182987 -0.10247746 153 5.9e-06 ***
##  7:     19.5      0.4790850     0.5454027 -0.05263313 153   0.0015 **
##  8:     22.5      0.4993464     0.5774717 -0.06609421 153   0.0013 **
##  9:     25.5      0.5483660     0.5978160 -0.03291924 153     0.054 .
## 10:     28.5      0.4993464     0.5710650 -0.06534653 153   0.0016 **
##     time  error.diff shapes
##  1:  1.5 -0.13741579     24
##  2:  4.5 -0.10090751     24
##  3:  7.5 -0.07478623     24
##  4: 10.5 -0.07040539     24
##  5: 13.5 -0.09361413     24
##  6: 16.5 -0.10247746     24
##  7: 19.5 -0.05263313     24
##  8: 22.5 -0.06609421     24
##  9: 25.5 -0.03291924     16
## 10: 28.5 -0.06534653     24

##     time.bin subj.diff.mean obj.diff.mean    error.diff   n        pval
##  1:      1.5      0.4415094     0.6007697 -1.658770e-01 106 3.8e-06 ***
##  2:      4.5      0.5119497     0.6324837 -1.343840e-01 159 4.2e-06 ***
##  3:      7.5      0.5100629     0.5479813 -4.895619e-02 159     0.069 .
##  4:     10.5      0.5220126     0.5177334  2.196993e-03 159     0.93 :(
##  5:     13.5      0.5169811     0.5303606 -2.035258e-02 159     0.43 :(
##  6:     16.5      0.5100629     0.5026471  2.226322e-05 159        1 :(
##  7:     19.5      0.4584906     0.4514766 -3.401739e-03 159     0.87 :(
##  8:     22.5      0.4226415     0.4287566 -1.335901e-02 159      0.6 :(
##  9:     25.5      0.4584906     0.3964332  6.936761e-02 159     0.013 *
## 10:     28.5      0.4446541     0.3652666  6.326623e-02 159     0.012 *
##     time    error.diff shapes
##  1:  1.5 -1.658770e-01     24
##  2:  4.5 -1.343840e-01     24
##  3:  7.5 -4.895619e-02     16
##  4: 10.5  2.196993e-03     16
##  5: 13.5 -2.035258e-02     16
##  6: 16.5  2.226322e-05     16
##  7: 19.5 -3.401739e-03     16
##  8: 22.5 -1.335901e-02     16
##  9: 25.5  6.936761e-02     24
## 10: 28.5  6.326623e-02     24

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74494  -0.17245  -0.05919   0.23401   0.58312  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.27589    0.03055    9.03  < 2e-16 ***
## timeNorm     0.08680    0.02952    2.94  0.00336 ** 
## obj.diff    -0.61333    0.03125  -19.63  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06170749)
## 
##     Null deviance: 82.864  on 927  degrees of freedom
## Residual deviance: 57.079  on 925  degrees of freedom
## AIC: 53.74
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.72188  -0.21303  -0.00161   0.20776   0.77339  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.14422    0.01739   8.294   <2e-16 ***
## timeNorm     0.05348    0.02125   2.517   0.0119 *  
## obj.diff    -0.36031    0.01960 -18.379   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07295822)
## 
##     Null deviance: 180.75  on 2116  degrees of freedom
## Residual deviance: 154.23  on 2114  degrees of freedom
## AIC: 470.76
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.77262  -0.17089  -0.00174   0.20032   0.69533  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.12749    0.01662    7.67 2.93e-14 ***
## timeNorm     0.02037    0.02144    0.95    0.342    
## obj.diff    -0.34664    0.02125  -16.31  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05766731)
## 
##     Null deviance: 111.211  on 1652  degrees of freedom
## Residual deviance:  95.151  on 1650  degrees of freedom
## AIC: -20.108
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5359375     0.7914831 -0.25851324 64 1.3e-08 ***
##  2:      4.5      0.5708333     0.7798456 -0.22545680 96 8.2e-09 ***
##  3:      7.5      0.6052083     0.7544085 -0.16335208 96 4.3e-06 ***
##  4:     10.5      0.6291667     0.7221817 -0.09614468 96    0.003 **
##  5:     13.5      0.6260417     0.7613665 -0.16372251 96 7.4e-06 ***
##  6:     16.5      0.6281250     0.7309465 -0.11601158 96 0.00072 ***
##  7:     19.5      0.6125000     0.7089551 -0.10745208 96 0.00047 ***
##  8:     22.5      0.6072917     0.7286953 -0.12187730 96 0.00046 ***
##  9:     25.5      0.5927083     0.6849442 -0.08718941 96     0.011 *
## 10:     28.5      0.6125000     0.6539723 -0.03781422 96     0.25 :(
##     time  error.diff shapes
##  1:  1.5 -0.25851324     24
##  2:  4.5 -0.22545680     24
##  3:  7.5 -0.16335208     24
##  4: 10.5 -0.09614468     24
##  5: 13.5 -0.16372251     24
##  6: 16.5 -0.11601158     24
##  7: 19.5 -0.10745208     24
##  8: 22.5 -0.12187730     24
##  9: 25.5 -0.08718941     24
## 10: 28.5 -0.03781422     16

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.5000000     0.5733496 -0.075648384 146   0.0046 **
##  2:      4.5      0.5520548     0.6435516 -0.084467252 219 6.9e-06 ***
##  3:      7.5      0.5118721     0.5237607 -0.017676852 219     0.38 :(
##  4:     10.5      0.5337900     0.5529666 -0.016029654 219      0.4 :(
##  5:     13.5      0.5347032     0.5429946 -0.009063747 219     0.64 :(
##  6:     16.5      0.4954338     0.5144682 -0.022588006 219     0.21 :(
##  7:     19.5      0.4926941     0.5151095 -0.026000186 219     0.16 :(
##  8:     22.5      0.4657534     0.4797826 -0.021965615 219     0.24 :(
##  9:     25.5      0.5200913     0.4857215  0.026431095 219      0.2 :(
## 10:     28.5      0.5000000     0.4710116  0.012688061 219     0.53 :(
##     time   error.diff shapes
##  1:  1.5 -0.075648384     24
##  2:  4.5 -0.084467252     24
##  3:  7.5 -0.017676852     16
##  4: 10.5 -0.016029654     16
##  5: 13.5 -0.009063747     16
##  6: 16.5 -0.022588006     16
##  7: 19.5 -0.026000186     16
##  8: 22.5 -0.021965615     16
##  9: 25.5  0.026431095     16
## 10: 28.5  0.012688061     16

##     time.bin subj.diff.mean obj.diff.mean    error.diff   n      pval
##  1:      1.5      0.4394737     0.5259072 -0.0735740034 114 0.0088 **
##  2:      4.5      0.4485380     0.4666732 -0.0156461353 171   0.46 :(
##  3:      7.5      0.4198830     0.4484831 -0.0246561009 171   0.21 :(
##  4:     10.5      0.4614035     0.4460741  0.0204785496 171   0.31 :(
##  5:     13.5      0.3871345     0.4108429 -0.0182435888 171   0.41 :(
##  6:     16.5      0.4029240     0.4045515 -0.0002120625 171   0.99 :(
##  7:     19.5      0.3953216     0.3939035 -0.0027650308 171   0.89 :(
##  8:     22.5      0.3982456     0.3939114  0.0062870968 171   0.74 :(
##  9:     25.5      0.4157895     0.3876650  0.0277159604 171   0.13 :(
## 10:     28.5      0.3584795     0.3430010  0.0142558446 171   0.46 :(
##     time    error.diff shapes
##  1:  1.5 -0.0735740034     24
##  2:  4.5 -0.0156461353     16
##  3:  7.5 -0.0246561009     16
##  4: 10.5  0.0204785496     16
##  5: 13.5 -0.0182435888     16
##  6: 16.5 -0.0002120625     16
##  7: 19.5 -0.0027650308     16
##  8: 22.5  0.0062870968     16
##  9: 25.5  0.0277159604     16
## 10: 28.5  0.0142558446     16

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.65081  -0.16600  -0.07689   0.21864   0.38438  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.29746    0.07745   3.841 0.000159 ***
## timeNorm     0.03979    0.04731   0.841 0.401279    
## obj.diff    -0.59239    0.08830  -6.709 1.52e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.03968561)
## 
##     Null deviance: 10.995  on 231  degrees of freedom
## Residual deviance:  9.088  on 229  degrees of freedom
## AIC: -85.242
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.6250000     0.8541813 -0.23116534 16   0.0013 **
##  2:      4.5      0.6375000     0.7984136 -0.16995810 24   0.0053 **
##  3:      7.5      0.6208333     0.7533950 -0.13245689 24     0.014 *
##  4:     10.5      0.6375000     0.7827081 -0.15599626 24   0.0079 **
##  5:     13.5      0.6250000     0.8239746 -0.20561865 24 4.4e-05 ***
##  6:     16.5      0.6375000     0.7813561 -0.15210779 24     0.027 *
##  7:     19.5      0.6541667     0.7252246 -0.07066985 24     0.14 :(
##  8:     22.5      0.6458333     0.7650575 -0.12329390 24     0.049 *
##  9:     25.5      0.6583333     0.7912822 -0.13403150 24   0.0072 **
## 10:     28.5      0.6166667     0.7394780 -0.11089775 24     0.042 *
##     time  error.diff shapes
##  1:  1.5 -0.23116534     24
##  2:  4.5 -0.16995810     24
##  3:  7.5 -0.13245689     24
##  4: 10.5 -0.15599626     24
##  5: 13.5 -0.20561865     24
##  6: 16.5 -0.15210779     24
##  7: 19.5 -0.07066985     16
##  8: 22.5 -0.12329390     24
##  9: 25.5 -0.13403150     24
## 10: 28.5 -0.11089775     24

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7128  -0.1799   0.0080   0.1979   0.6542  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.160601   0.040886   3.928 9.46e-05 ***
## timeNorm     0.003705   0.038216   0.097    0.923    
## obj.diff    -0.347965   0.054087  -6.433 2.39e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07289063)
## 
##     Null deviance: 51.554  on 666  degrees of freedom
## Residual deviance: 48.399  on 664  degrees of freedom
## AIC: 151.12
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.5413043     0.6313063 -0.080414494 46   0.073 .
##  2:      4.5      0.5652174     0.6292224 -0.057371089 69   0.099 .
##  3:      7.5      0.5420290     0.5592216 -0.011452588 69   0.74 :(
##  4:     10.5      0.5463768     0.5820863 -0.022036607 69   0.57 :(
##  5:     13.5      0.5550725     0.5449914  0.012093320 69   0.72 :(
##  6:     16.5      0.5478261     0.5622564 -0.019457251 69   0.62 :(
##  7:     19.5      0.4942029     0.5766338 -0.086681165 69 0.0077 **
##  8:     22.5      0.4681159     0.5121072 -0.050443461 69   0.17 :(
##  9:     25.5      0.5014493     0.4988278 -0.003887111 69   0.93 :(
## 10:     28.5      0.5014493     0.4985043 -0.010272074 69    0.7 :(
##     time   error.diff shapes
##  1:  1.5 -0.080414494     16
##  2:  4.5 -0.057371089     16
##  3:  7.5 -0.011452588     16
##  4: 10.5 -0.022036607     16
##  5: 13.5  0.012093320     16
##  6: 16.5 -0.019457251     16
##  7: 19.5 -0.086681165     24
##  8: 22.5 -0.050443461     16
##  9: 25.5 -0.003887111     16
## 10: 28.5 -0.010272074     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.61019  -0.15879   0.00778   0.17071   0.53696  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.13601    0.02536   5.362 1.08e-07 ***
## timeNorm     0.01638    0.02758   0.594    0.553    
## obj.diff    -0.23883    0.03902  -6.121 1.47e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0443567)
## 
##     Null deviance: 36.420  on 782  degrees of freedom
## Residual deviance: 34.598  on 780  degrees of freedom
## AIC: -212.38
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.5185185     0.5012163 0.020987089 54   0.54 :(
##  2:      4.5      0.4827160     0.4544613 0.033464592 81   0.19 :(
##  3:      7.5      0.4617284     0.4632778 0.001764609 81   0.95 :(
##  4:     10.5      0.5086420     0.4331719 0.087371264 81 0.0012 **
##  5:     13.5      0.4493827     0.4045805 0.051873659 81   0.053 .
##  6:     16.5      0.4851852     0.4351713 0.052762168 81   0.042 *
##  7:     19.5      0.4728395     0.4419957 0.028567591 81   0.25 :(
##  8:     22.5      0.4567901     0.3970833 0.064184236 81   0.019 *
##  9:     25.5      0.4283951     0.3766636 0.052786293 81   0.026 *
## 10:     28.5      0.4086420     0.3332279 0.073327560 81 0.0036 **
##     time  error.diff shapes
##  1:  1.5 0.020987089     16
##  2:  4.5 0.033464592     16
##  3:  7.5 0.001764609     16
##  4: 10.5 0.087371264     24
##  5: 13.5 0.051873659     16
##  6: 16.5 0.052762168     24
##  7: 19.5 0.028567591     16
##  8: 22.5 0.064184236     24
##  9: 25.5 0.052786293     24
## 10: 28.5 0.073327560     24

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74322  -0.20483  -0.03202   0.20676   0.62415  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.20999    0.04489   4.678 4.47e-06 ***
## timeNorm     0.04941    0.05280   0.936     0.35    
## obj.diff    -0.51260    0.04492 -11.413  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06276797)
## 
##     Null deviance: 26.320  on 289  degrees of freedom
## Residual deviance: 18.014  on 287  degrees of freedom
## AIC: 25.159
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n     pval
##  1:      1.5      0.5200000     0.6508420 -0.15316874 20  0.076 .
##  2:      4.5      0.5233333     0.6781967 -0.15793428 30  0.011 *
##  3:      7.5      0.5600000     0.7239238 -0.17383717 30 0.004 **
##  4:     10.5      0.6166667     0.7072602 -0.09559555 30   0.1 :(
##  5:     13.5      0.6300000     0.7376158 -0.09754771 30  0.045 *
##  6:     16.5      0.5033333     0.6329179 -0.17388810 30  0.022 *
##  7:     19.5      0.5666667     0.6721874 -0.14418598 30  0.064 .
##  8:     22.5      0.6766667     0.7257057 -0.04280707 30  0.54 :(
##  9:     25.5      0.5200000     0.6342124 -0.10335371 30  0.088 .
## 10:     28.5      0.5400000     0.6167904 -0.06210791 30  0.32 :(
##     time  error.diff shapes
##  1:  1.5 -0.15316874     16
##  2:  4.5 -0.15793428     24
##  3:  7.5 -0.17383717     24
##  4: 10.5 -0.09559555     16
##  5: 13.5 -0.09754771     24
##  6: 16.5 -0.17388810     24
##  7: 19.5 -0.14418598     16
##  8: 22.5 -0.04280707     16
##  9: 25.5 -0.10335371     16
## 10: 28.5 -0.06210791     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74943  -0.17271   0.02931   0.16064   0.81301  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.03489    0.02687   1.298   0.1945    
## timeNorm     0.06011    0.03525   1.705   0.0886 .  
## obj.diff    -0.20242    0.02776  -7.291 8.41e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0672497)
## 
##     Null deviance: 50.406  on 695  degrees of freedom
## Residual deviance: 46.604  on 693  degrees of freedom
## AIC: 101.41
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.5291667     0.6078474 -0.086598352 48    0.04 *
##  2:      4.5      0.5944444     0.6846758 -0.062208797 72 0.0014 **
##  3:      7.5      0.4833333     0.5068404 -0.039510796 72   0.25 :(
##  4:     10.5      0.5347222     0.6049774 -0.057714866 72   0.048 *
##  5:     13.5      0.4944444     0.5748430 -0.068004375 72 0.0076 **
##  6:     16.5      0.4694444     0.5248235 -0.047269189 72   0.13 :(
##  7:     19.5      0.5166667     0.5374851 -0.005537462 72   0.81 :(
##  8:     22.5      0.5069444     0.5711729 -0.055336688 72   0.044 *
##  9:     25.5      0.6111111     0.6185741 -0.004538522 72    0.8 :(
## 10:     28.5      0.5680556     0.5944001 -0.028304406 72   0.22 :(
##     time   error.diff shapes
##  1:  1.5 -0.086598352     24
##  2:  4.5 -0.062208797     24
##  3:  7.5 -0.039510796     16
##  4: 10.5 -0.057714866     24
##  5: 13.5 -0.068004375     24
##  6: 16.5 -0.047269189     16
##  7: 19.5 -0.005537462     16
##  8: 22.5 -0.055336688     24
##  9: 25.5 -0.004538522     16
## 10: 28.5 -0.028304406     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.70974  -0.14355  -0.04811   0.22707   0.79366  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.006679   0.029713   0.225    0.822    
## timeNorm     0.038072   0.041830   0.910    0.363    
## obj.diff    -0.291208   0.031993  -9.102   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06709016)
## 
##     Null deviance: 38.482  on 492  degrees of freedom
## Residual deviance: 32.874  on 490  degrees of freedom
## AIC: 72.117
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.3500000     0.5491058 -0.21304774 34   9e-04 ***
##  2:      4.5      0.3745098     0.5099408 -0.11854584 51 0.00081 ***
##  3:      7.5      0.3843137     0.4761745 -0.07531510 51     0.021 *
##  4:     10.5      0.4313725     0.5014928 -0.07835851 51     0.014 *
##  5:     13.5      0.3333333     0.4758000 -0.13976005 51 0.00025 ***
##  6:     16.5      0.3000000     0.4416642 -0.13413221 51 9.5e-05 ***
##  7:     19.5      0.3745098     0.4820011 -0.08242696 51 0.00086 ***
##  8:     22.5      0.3843137     0.4991676 -0.09930056 51     0.012 *
##  9:     25.5      0.4764706     0.5471006 -0.04133532 51     0.12 :(
## 10:     28.5      0.3784314     0.5112239 -0.11440884 51   5e-04 ***
##     time  error.diff shapes
##  1:  1.5 -0.21304774     24
##  2:  4.5 -0.11854584     24
##  3:  7.5 -0.07531510     24
##  4: 10.5 -0.07835851     24
##  5: 13.5 -0.13976005     24
##  6: 16.5 -0.13413221     24
##  7: 19.5 -0.08242696     24
##  8: 22.5 -0.09930056     24
##  9: 25.5 -0.04133532     16
## 10: 28.5 -0.11440884     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7180  -0.1523  -0.0701   0.2646   0.5316  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.36438    0.05390   6.761 4.83e-11 ***
## timeNorm     0.11540    0.04908   2.351   0.0192 *  
## obj.diff    -0.74698    0.05334 -14.004  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07166686)
## 
##     Null deviance: 45.424  on 405  degrees of freedom
## Residual deviance: 28.882  on 403  degrees of freedom
## AIC: 87.062
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.4964286     0.8561134 -0.36716892 28 7.5e-08 ***
##  2:      4.5      0.5666667     0.8418417 -0.29004109 42   9e-07 ***
##  3:      7.5      0.6285714     0.7767624 -0.16850164 42   0.0071 **
##  4:     10.5      0.6333333     0.6982535 -0.07050893 42     0.21 :(
##  5:     13.5      0.6238095     0.7425551 -0.16270974 42     0.023 *
##  6:     16.5      0.7119048     0.7721614 -0.07008744 42     0.23 :(
##  7:     19.5      0.6214286     0.7259209 -0.11110926 42     0.023 *
##  8:     22.5      0.5357143     0.7100524 -0.19151750 42   0.0023 **
##  9:     25.5      0.6071429     0.6604165 -0.04212140 42     0.55 :(
## 10:     28.5      0.6619048     0.6316703  0.02540027 42     0.62 :(
##     time  error.diff shapes
##  1:  1.5 -0.36716892     24
##  2:  4.5 -0.29004109     24
##  3:  7.5 -0.16850164     24
##  4: 10.5 -0.07050893     16
##  5: 13.5 -0.16270974     24
##  6: 16.5 -0.07008744     16
##  7: 19.5 -0.11110926     24
##  8: 22.5 -0.19151750     24
##  9: 25.5 -0.04212140     16
## 10: 28.5  0.02540027     16
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.64743  -0.21112  -0.01143   0.18810   0.69300  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.24374    0.02866   8.503   <2e-16 ***
## timeNorm     0.04799    0.03619   1.326    0.185    
## obj.diff    -0.53789    0.03229 -16.657   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07228863)
## 
##     Null deviance: 76.556  on 753  degrees of freedom
## Residual deviance: 54.289  on 751  degrees of freedom
## AIC: 163.93
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.4365385     0.4902360 -0.056484920 52   0.23 :(
##  2:      4.5      0.5012821     0.6182667 -0.127211343 78 0.0017 **
##  3:      7.5      0.5115385     0.5080102 -0.009983459 78   0.85 :(
##  4:     10.5      0.5217949     0.4791968  0.031134779 78   0.36 :(
##  5:     13.5      0.5538462     0.5118296  0.042413783 78   0.27 :(
##  6:     16.5      0.4730769     0.4626353 -0.001909564 78   0.96 :(
##  7:     19.5      0.4692308     0.4400298  0.018917033 78    0.6 :(
##  8:     22.5      0.4256410     0.3668275  0.061870825 78    0.2 :(
##  9:     25.5      0.4525641     0.3514944  0.103712580 78 0.0095 **
## 10:     28.5      0.4358974     0.3327941  0.093319755 78   0.026 *
##     time   error.diff shapes
##  1:  1.5 -0.056484920     16
##  2:  4.5 -0.127211343     24
##  3:  7.5 -0.009983459     16
##  4: 10.5  0.031134779     16
##  5: 13.5  0.042413783     16
##  6: 16.5 -0.001909564     16
##  7: 19.5  0.018917033     16
##  8: 22.5  0.061870825     16
##  9: 25.5  0.103712580     24
## 10: 28.5  0.093319755     24

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.59428  -0.20393   0.00244   0.19029   0.63266  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.20091    0.03687   5.449  9.2e-08 ***
## timeNorm    -0.02360    0.04793  -0.492    0.623    
## obj.diff    -0.50212    0.04973 -10.097  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05609687)
## 
##     Null deviance: 27.59  on 376  degrees of freedom
## Residual deviance: 20.98  on 374  degrees of freedom
## AIC: -11.147
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n    pval
##  1:      1.5      0.3923077     0.5468516 -0.14292222 26 0.043 *
##  2:      4.5      0.4743590     0.4354555  0.03036380 39 0.64 :(
##  3:      7.5      0.3794872     0.3815438 -0.01099869 39 0.79 :(
##  4:     10.5      0.4025641     0.4004002  0.01597914 39 0.85 :(
##  5:     13.5      0.3282051     0.3389055 -0.01793324 39 0.63 :(
##  6:     16.5      0.3666667     0.2924247  0.07494671 39 0.11 :(
##  7:     19.5      0.2615385     0.1788149  0.07887150 39 0.14 :(
##  8:     22.5      0.2948718     0.2496809  0.01582368 39 0.58 :(
##  9:     25.5      0.3102564     0.2020211  0.11168377 39  0.06 .
## 10:     28.5      0.2282051     0.1433153  0.06983217 39 0.26 :(
##     time  error.diff shapes
##  1:  1.5 -0.14292222     24
##  2:  4.5  0.03036380     16
##  3:  7.5 -0.01099869     16
##  4: 10.5  0.01597914     16
##  5: 13.5 -0.01793324     16
##  6: 16.5  0.07494671     16
##  7: 19.5  0.07887150     16
##  8: 22.5  0.01582368     16
##  9: 25.5  0.11168377     16
## 10: 28.5  0.06983217     16

{r plot.subjective.objective.difficulty.confidence.scale, echo=FALSE} # #-------------------------------------------------------------------------------------- # # SHOWING SUBJECTIVE VS OBJECTIVE DIFFICULTY (CONFIDENCE SCALE APPROACH) # #-------------------------------------------------------------------------------------- # # plot.subjective.difficulty <- function(DT,selGroup,title){ # # print(selGroup) # # # Lien entre mise normalisée et difficultée estimée (hard / easy effect) # obj.diff.quants = seq(0,1,1/16)#quantile(DT$obj.diff, probs=(seq(0,1,0.05))) # nb.bins = length(obj.diff.quants)-1 # subj.diff.med = numeric(nb.bins) # obj.diff.bin = numeric(nb.bins) # obj.diff.bin.cur = 0; # test.pvals = numeric(nb.bins) # conf.min = numeric(nb.bins) # conf.max = numeric(nb.bins) # nb.vals = numeric(nb.bins) # shapes = numeric(nb.bins) # delta.obj.subj = numeric(nb.bins) # hist(DT$obj.diff) # for(i in 1:nb.bins){ # #obj.diff.bin.cur = round(i/10,1) # #subj.diff = DT[round(obj.diff,1)==obj.diff.bin.cur]$subj.diff.mise # obj.diff.bin.cur = (obj.diff.quants[i] + obj.diff.quants[i+1])/2.0 # #subj.diff = DT[obj.diff > obj.diff.quants[i] & obj.diff<=obj.diff.quants[i+1]]$subj.diff.mise # DTLoc = DT[obj.diff > obj.diff.quants[i] & obj.diff<=obj.diff.quants[i+1]] # if(selGroup != "all") # DTLoc = DTLoc[niveau.group==selGroup] # DTLoc = DTLoc[,.(confiance.mean=mean(subj.diff.confiance)),by=IDjoueur] # subj.diff = DTLoc$confiance.mean # obj.diff.bin[i] = obj.diff.bin.cur # subj.diff.med[i] = NA # test.pvals[i] = NA # conf.min[i] = NA # conf.max[i] = NA # delta.obj.subj[i] = NA # shapes[i] = 16 # nb.vals[i] = length(subj.diff) # if(nb.vals[i] > 1){ # try.res = try(test.res <- wilcox.test(subj.diff,mu = obj.diff.bin.cur,conf.int=T)) # if (class(try.res) != "try-error"){ # #print(test.res) # #hist(subj.diff) # test.pvals[i] = format.pval.stars(test.res$p.value) # if(test.res$p.value < 0.05) # shapes[i] = 24 # #subj.diff.med[i] = mean(subj.diff) # subj.diff.med[i] = test.res$estimate # conf.min[i] = test.res$conf.int[1] # conf.max[i] = test.res$conf.int[2] # delta.obj.subj[i] = signif(subj.diff.med[i] - obj.diff.bin.cur,digit=2) # } # } # } # # #print table of pvalues # print(data.table(obj.diff.bin=obj.diff.bin,delta.obj.subj=delta.obj.subj,n=nb.vals,pval=test.pvals)) # # #summary # print("mean and sd of nb players per bin") # DTNbVals = data.table(nb = nb.vals, pval=test.pvals) # print(DTNbVals[!is.na(pval)]) # print(signif(mean(DTNbVals[!is.na(pval)]$nb),digits=3)) # print(signif(sd(DTNbVals[!is.na(pval)]$nb),digits=3)) # # #kernel smooth # subj.diff.smooth <- ksmooth(x=DT$obj.diff,y=DT$subj.diff.confiance,bandwidth = 0.2) # DTSmooth = data.table(x=subj.diff.smooth$x,y=subj.diff.smooth$y) # # DTPlot = data.table(obj.diff=obj.diff.bin,subj.diff=subj.diff.med, shapes=shapes) # # p = ggplot() + ggtitle(title) + # # geom_line(aes(x=DTPouet$x,y=DTPouet$y))+ # geom_point(aes(x=DTPlot$obj.diff,y=DTPlot$subj.diff),alpha = 1, size = 3, shape=DTPlot$shapes) + # xlim(0,1)+ # ylim(0,1)+ # geom_errorbar(aes(x=DTPlot$obj.diff, ymin=conf.min, ymax=conf.max), width=.01,color="red") + # geom_abline(intercept = 0, slope = 1, color="blue") + # xlab("Objective Difficulty") + ylab("Subjective Difficulty") + theme(text = element_text(size=15)) # # print(p) # } #

All tasks

{r plot.subjective.difficulty.all.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTAll,"all", "All tasks, all groups") # plot.subjective.difficulty(DTAll,"good", "All tasks, good") # plot.subjective.difficulty(DTAll,"medium", "All tasks, medium") # plot.subjective.difficulty(DTAll,"bad", "All tasks, bad") #

Motor task

{r plot.subjective.difficulty.motor.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTM,"all", "Motor, all") # plot.subjective.difficulty(DTM,"good", "Motor, good") # plot.subjective.difficulty(DTM,"medium", "Motor, medium") # plot.subjective.difficulty(DTM,"bad", "Motor, bad") #

Sensory task

{r plot.subjective.difficulty.sensory.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTS,"all","Sensory, all") # plot.subjective.difficulty(DTS,"good","Sensory, good") # plot.subjective.difficulty(DTS,"medium","Sensory, medium") # plot.subjective.difficulty(DTS,"bad","Sensory, bad") #

Logical task

{r plot.subjective.difficulty.logical.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTL,"all","Logical, all") # plot.subjective.difficulty(DTL,"good","Logical, good") # plot.subjective.difficulty(DTL,"medium","Logical, medium") # plot.subjective.difficulty(DTL,"bad","Logical, bad") #